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| 001 | 917618 | ||
| 005 | 20240712112858.0 | ||
| 024 | 7 | _ | |a 10.1002/aic.17971 |2 doi |
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| 037 | _ | _ | |a FZJ-2023-00813 |
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| 100 | 1 | _ | |a Rittig, Jan G. |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Graph machine learning for design of high‐octane fuels |
| 260 | _ | _ | |a Hoboken, NJ |c 2023 |b Wiley |
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| 520 | _ | _ | |a Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data. |
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| 700 | 1 | _ | |a Ritzert, Martin |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Schweidtmann, Artur M. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Winkler, Stefanie |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Weber, Jana M. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Morsch, Philipp |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Heufer, Karl Alexander |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Grohe, Martin |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 8 |u fzj |
| 700 | 1 | _ | |a Dahmen, Manuel |0 P:(DE-Juel1)172097 |b 9 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1002/aic.17971 |0 PERI:(DE-600)2020333-0 |n 4 |p e17971 |t AIChE journal |v 69 |y 2023 |x 0001-1541 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/917618/files/AIChE%20Journal%20-%202022%20-%20Rittig%20-%20Graph%20machine%20learning%20for%20design%20of%20high%E2%80%90octane%20fuels.pdf |
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